The automated localization of moving vehicles and machine-based remote sensing of vehicle local environment is becoming increasingly important in several different disciplines. One such discipline is automotive transportation. In recent years, many cars and trucks implement onboard Global Positioning System (GPS) receivers and navigation systems utilizing GPS data for driver guidance. However, as automobile manufacturers seek to implement more advanced driving automation, such as autonomous driving features, GPS-based location systems may not be able to provide sufficiently accurate vehicle localization, nor do they allow for real-time sensing of a vehicle's local environment. Therefore, supplemental sensing systems may be desirable, as well as highly detailed infrastructure and landmark maps, potentially including three-dimensional semantic maps.
Another application in which vehicle localization, sensing of a local environment and three-dimensional semantic maps may be desirable is in the operation of trains. The U.S. Congress passed the U.S. Rail Safety Improvement Act in 2008 to ensure all trains are monitored in real time to enable “Positive Train Control” (PTC). This law requires that all trains report their location information such that all train movements are tracked in real time. PTC is required to function both in signaled territories and dark territories.
In order to achieve this milestone, numerous companies have tried to implement various PTC systems. A reoccurring problem is that current PTC systems can only track a train when it passes by wayside transponders or signaling stations along a railway line, rendering the operators unaware of the status of the train in between wayside signals. Therefore, the distance between consecutive physical wayside signaling infrastructures determines the minimum safe distance required between trains (headway). Current signaling infrastructure also limits the scope of deploying wayside signaling equipment due to the cost and complexity of constructing and maintaining PTC infrastructure along the length of the railway network. The current methodology for detecting trains the last time they passed near a wayside detector suffers from a lack of position information in-between transponders.
Certain companies went a step further to utilize radio towers along the length of the operator's track network to create virtual signals between trains, circumventing the need for wayside signaling equipment. Radio towers still require signaling equipment to be deployed in order for the radio communication to take place. However, for dependable location information, additional transponders have to be deployed along tracks for the train to reliably determine the position of the train and the track it is currently occupying.
One example of a PTC system in use is the European Train Control System (ETCS) which relies on trackside equipment and a train-mounted control that reacts to the information related to the signaling. That system relies heavily on infrastructure that has not been deployed in the United States or in developing countries.
A solution that requires minimal deployment of wayside signaling equipment would be beneficial for establishing Positive Train Control throughout the United States and in the developing world. Deploying millions of balises—the transponders used to detect and communicate the presence of trains and their location—every 1-15 km along tracks is less effective because balises are negatively affected by environmental conditions, theft, and require regular maintenance, and the data collected may not be used in real time. Obtaining positional data through only trackside equipment is not a scalable solution considering the costs of utilizing balises throughout the entire railway network PTC. Moreover, train control and safety systems cannot rely solely on a global positioning system (GPS) as it not sufficiently accurate to distinguish between tracks, thereby requiring wayside signaling for position calibration.
As autonomous driving, train control and other vehicle operating systems evolve, these and other challenges may be addressed by systems and methods described hereinbelow.